Traditional　saliency　object　detection　methods,　assuming　that　there　is　only　one　salient　object,　is　not　conductive　to　practical　application.　Their　effects　are　dependent　on　saliency　threshold.　Object　detection　model　provides　a　kind　of　new　solutions.　SSD　can　accurately　detect　multi-objects　with　different　scales　simultaneously,　except　for　small　objects.　To　overcome　this　drawback,　this　paper　presents　a　new　multi-　saliency　objects　detection　model,　DAR-SSD,　appending　a　deconvolution　module　embedded　with　an　attention　residual　module.　Experiments　show　that　DAR-SSD　achieves　a　higher　detection　accuracy　than　SOD.　Also,　it　improves　detection　performance　for　multi-　saliency　objects　on　small　scales,　compared　with　original　SSD,　and　it　has　an　advantage　over　complicated　background,　compared　with　MDF　and　DCL,　which　also　are　deep　model　based　methods.　©　2018,　Science　Press.　All　right　reserved.